Towards Speech-only Opinion-level Sentiment Analysis

Annalena Aicher, Alisa Gazizullina, Aleksei Gusev, Yuri Matveev, Wolfgang Minker


Abstract
The growing popularity of various forms of Spoken Dialogue Systems (SDS) raises the demand for their capability of implicitly assessing the speaker’s sentiment from speech only. Mapping the latter on user preferences enables to adapt to the user and individualize the requested information while increasing user satisfaction. In this paper, we explore the integration of rank consistent ordinal regression into a speech-only sentiment prediction task performed by ResNet-like systems. Furthermore, we use speaker verification extractors trained on larger datasets as low-level feature extractors. An improvement of performance is shown by fusing sentiment and pre-extracted speaker embeddings reducing the speaker bias of sentiment predictions. Numerous experiments on Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) databases show that we beat the baselines of state-of-the-art unimodal approaches. Using speech as the only modality combined with optimizing an order-sensitive objective function gets significantly closer to the sentiment analysis results of state-of-the-art multimodal systems.
Anthology ID:
2022.lrec-1.215
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2000–2006
Language:
URL:
https://aclanthology.org/2022.lrec-1.215
DOI:
Bibkey:
Cite (ACL):
Annalena Aicher, Alisa Gazizullina, Aleksei Gusev, Yuri Matveev, and Wolfgang Minker. 2022. Towards Speech-only Opinion-level Sentiment Analysis. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2000–2006, Marseille, France. European Language Resources Association.
Cite (Informal):
Towards Speech-only Opinion-level Sentiment Analysis (Aicher et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.215.pdf
Data
CMU-MOSEI